A FUZZY NEURAL NETWORK MODEL FOR ANALYZING BALTIC DRY INDEX IN THE BULK MARITIME INDUSTRY

Baltic Dry Index (BDI) is one of the important indexes in the dry bulk shipping market. BDI analysis and forecasting is one of important activities of shipowners, charterers, shipping carriers, importers and exporters, and banks in the dry bulk shipping market. Based on the accurate BDI analysis and forecasting , the shipowners, charterers, shipping carriers, importers and exporters, and banks in the bulk shipping market could make many important decisions of shipping operation, management and financial invest such as building a new bulk carrier, chartering-in or chartering-out a second-hand bulk carrier, demolishing an old bulk carrier, and providing funding for shipowners. Thus, this paper adopts a fuzzy neural network model to analyze the relationship between the BDI in the international bulk shipping market and the major economic indexes in the global financial market. Finally, the proposed fuzzy neural network model is tested by empirical data during the period of 2000-2015. The results show that the fuzzy neural network model has high accuracy of forecasting. The fuzzy neural network model in this study seems to be promising and the model could help the shipowners, charterers, shipping carriers, importers and exporters, and banks forecast future BDI points in the bulk shipping market, and then make important decisions and operation strategies of shipping operation, management and financial invest.

OPPORTUNITIES FOR IMPROVED EFFICIENCY AND REDUCED CO2 EMISSIONS IN DRY BULK SHIPPING STEMMING FROM THE RELAXATION OF THE PANAMAX BEAM CONSTRAINT

In 2014 the Panama Canal Authority is scheduled to bring into commission new locks that will eliminate the long standing Panamax beam constraint of 32.2m. The expansion of the canal is aimed at increased capacity for container transits but will clearly have consequences for all types of vessel. There is an emerging demand for dry bulk carriers that are larger than the current Panamax limit of around 85,000 dwt but smaller than the Capesize class of around 160,000 dwt and the expansion of the canal will facilitate this development. Larger vessels will permit economies of scale and greater efficiency in the dry bulk shipping sector compared to what is currently possible with conventional Panamax ships. The relaxation of the constraint will additionally permit the development of more efficient hull forms than is possible within the existing beam constraint and the expansion of the Panama Canal’s locks will therefore (eventually) contribute directly to the reduction of CO2 produced by dry bulk shipping. The use of the Panamax constraint is far wider than the dry bulk sector, however, and the potential for reduction in carbon emissions for other sectors currently constrained to 32.2m beam is recommended for further study to evaluate the total carbon reduction ‘windfall’ that could result from the expansion of the Canal.

Sentiment-Augmented Supply and Demand Equations for the Dry Bulk Shipping Market

We present, for the first time in the literature, empirical estimates of the supply and demand curves for the ocean-going dry bulk sector, using a three-stage least squares methodology. Furthermore, we augment these functions with sentiment, which appears to have a positive and significant impact on supply. This supports the view that the outlook that shipowners have about the market will undoubtedly influence their decisions regarding purchasing vessels or bringing them out of lay up. Thus, our results highlight the fact that future expectations have an impact on current pricing, albeit indirectly, through their impact on the supply side. Our results further enhance the behavioral economics literature and provide important insights for both academics and professionals.

Spectral analysis of the dry bulk shipping market by utilizing the system dynamics approach

PurposeThis research analyzes the cycle of the dry bulk shipping market (DBSM) as a representative of spot and period charter rates in dry bulk shipping to develop strategies for investment timing (i.e. asset play) and fleet trading (chartering strategy).Design/methodology/approachSpectral analysis is a numerical approach to extract significant cyclicality, which may be utilized to develop trading strategies. Instead of working with a single dataset (univariate), a system approach can be utilized to observe a significant shipping market cycle in its multi-variate circumstance. In this paper, a system dynamics design is employed to extract cyclicality in the DBSM in its particular industrial environment. The system dynamic design has competitive forecasting accuracy relative to univariate time series models and artificial neural networks (ANNs) in terms of forecasting outcomes.FindingsThe results show that the system dynamic design has a better forecasting performance according to three evaluation metrics, mean absolute scale error (MASE), root mean square error (RMSE) and mean absolute percentage error (MAPE).Originality/valueCyclical analysis is a significantly useful instrument for shipping asset management, particularly in market entry–exit operations. This paper investigated the cyclical nature of the dry bulk shipping business and estimated significant business cycle periodicity at around 4.5-year frequency (i.e. the Kitchin cycle).

A Chaos Analysis of the Dry Bulk Shipping Market

Finding low-dimensional chaos is a relevant issue as it could allow short-term reliable forecasting. However, the existence of chaos in shipping freight rates remains an open and outstanding matter as previous research used methodology that can produce misleading results. Using daily data, this paper aims to unveil the nonlinear dynamics of the Baltic Dry Index that has been proposed as a measure of the shipping rates for certain raw materials. We tested for the existence of nonlinearity and low-dimensional chaos. We have also examined the chaotic dynamics throughout three sub-sampling periods, which have been determined by the volatility pattern of the series. For this purpose, from a comprehensive view we apply several metric and topological techniques, including the most suitable methods for noisy time series analysis. The proposed methodology considers the characteristics of chaotic time series, such as nonlinearity, determinism, sensitivity to initial conditions, fractal dimension and recurrence. Although there is strong evidence of a nonlinear structure, a chaotic and, therefore, deterministic behavior cannot be assumed during the whole or the three periods considered. Our findings indicate that the generalized autoregressive conditional heteroscedastic (GARCH) model and exponential GARCH (EGARCH) model explain a significant part of the nonlinear structure that is found in the dry bulk shipping freight market.

Data Analysis of Shipping Cycle in Dry Bulk Market

The dry bulk market BDI index fell from 11,793 points on May 20, 2008 to 290 points on February 10, 2016, which is a very certain "downward cycle" of the shipping market, showing an "L" shape. By citing random samples and big data, the paper analyzes the causes of the "L" type cycle. First, through random sample data, a binary measurement model with BDI index as the dependent variable and dry bulk market supply and demand as independent variables is established, and it is concluded that the "down cycle" of the dry bulk market is caused by the "supply factor"; then the paper continues to quote big data, that is, to use "whole sample" to further analyze the sub-variables of supply variables. Finally, the paper concludes that the "downward cycle" of the dry bulk shipping market is "L" shaped, which is caused by changes in the "market structure" of the shipbuilding market.